Stores the output of Bayesian variable selection, as produced by
function `modelSelection`

.
The class extends a list, so all usual methods for lists also work for
`msfit`

objects, e.g. accessing elements, retrieving names etc.

Some additional methods are provided for printing information on screen, computing posterior probabilities or sampling from the posterior of regression coefficients, as indicated below.

Typically objects are automatically created by a call to `modelSelection`

.
Alternatively, objects can be created by calls of the form
`new("msfit",x)`

where `x`

is a list with the adequate
elements (see slots).

The class extends a list with elements:

- postSample
`matrix`

with posterior samples for the model indicator.`postSample[i,j]==1`

indicates that variable j was included in the model in the MCMC iteration i- postOther
`postOther`

returns posterior samples for parameters other than the model indicator, i.e. basically hyper-parameters. If hyper-parameters were fixed in the model specification,`postOther`

will be empty.- margpp
Marginal posterior probability for inclusion of each covariate. This is computed by averaging marginal post prob for inclusion in each Gibbs iteration, which is much more accurate than simply taking

`colMeans(postSample)`

.

- postMode
Model with highest posterior probability amongst all those visited

- postModeProb
Unnormalized posterior prob of posterior mode (log scale)

- postProb
Unnormalized posterior prob of each visited model (log scale)

- coef
Estimated coefficients (via posterior mode) for highest posterior probability model

- family
Residual distribution, i.e. argument

`family`

when calling`modelSelection`

- p
Number of variables

- show
`signature(object = "msfit")`

: Displays general information about the object.- postProb
`signature(object = "msfit")`

: Extracts posterior model probabilities.- rnlp
`signature(object = "msfit")`

: Obtain posterior samples for regression coefficients.

David Rossell

Johnson VE, Rossell D. Non-Local Prior Densities for Default Bayesian Hypothesis Tests. Journal of the Royal Statistical Society B, 2010, 72, 143-170

Johnson VE, Rossell D. Bayesian model selection in high-dimensional settings. Journal of the American Statistical Association, 107, 498:649-660.

See also `modelSelection`

and `rnlp`

.

1 | ```
showClass("msfit")
``` |

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